Analyze music/audio files locally without external APIs. Extract tempo (BPM), key estimate, section boundaries, loudness dynamics, spectral/timbre descriptors, and temporal mood journeys. Use when asked to 'hear the music,' audit tracks, compare mixes, inspect structure, or generate producer-facing notes from audio files.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install adam-researchh/music-analysisInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
Analyze audio files using signal processing. Two tools:
python3 skills/music-analysis/scripts/analyze_music.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/analyze_music.py track.mp3 --json
Reports: duration, sample rate, tempo (BPM), key estimate, energy stats (RMS mean/std/p95), spectral summary (centroid, rolloff, contrast), coarse section boundaries.
python3 skills/music-analysis/scripts/temporal_listen.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/temporal_listen.py track.mp3 --json
Reports: sliding-window analysis (4s windows, 2s hops) producing a full timeline of:
No AI/ML models — pure signal processing via librosa:
| Mood | Condition |
|---|---|
| submerged | Low energy, dark frequencies |
| ethereal | Low energy, high harmonic ratio |
| breathing | Low energy, other |
| simmering | Mid-low energy, warm |
| restless | Mid-low energy, high onset density |
| floating | Mid-low energy, bright |
| driving | Mid energy, percussive |
| soaring | Mid energy, harmonic... |
These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Music Analysis (Local, No External APIs) Analyze audio files using signal processing. Two tools: ## 1. Quick Analysis — snapshot of the whole track ```bash python3 skills/music-analysis/scripts/analyze_music.py /path/to/audio.mp3 python3 skills/music-analysis/scripts/analyze_music.py track.mp3 --json ``` **Reports:** duration, sample rate, tempo (BPM), key estimate, energy stats (RMS mean/std/p95), spectral summary (centroid, rolloff, contrast), coarse section boundaries. ## 2. Temporal Listen — experience the track as a journey ```bash python3 skills/music-analysis/scripts/temporal_listen.py /path/to/audio.mp3 python3 skills/music-analysis/scripts/temporal_listen.py track.mp3 --json ``` **Reports:** sliding-window analysis (4s windows, 2s hops) producing a full timeline of: - Energy level (relative to track average) - Mood labels (simmering, soaring, erupting, full force, submerged, ethereal, etc.) - Transitions (drop hits, pulls back, shifts color, evolves) - Texture decomposition (harmonic/percussive ratio, onset density, roughness) - Tension model (sustained intensity tracking) - Narrative arc (mountain, ascending, descending, plateau, wave) - Peak moment, quietest moment, mood journey summary ### How it works No AI/ML models — pure signal processing via librosa: - **RMS energy** per window (relative to global average) - **Spectral centroid** (brightness), rolloff, flatness - **Harmonic/percussive source separation** (HPSS) - **Onset detection** (rhythmic activity density) - **Zero-crossing rate** (texture roughness) - Mood labels are rule-based mappings from these features ### Mood vocabulary | Mood | Condition | |------|-----------| | submerged | Low energy, dark frequencies | | ethereal | Low energy, high harmonic ratio | | breathing | Low energy, other | | simmering | Mid-low energy, warm | | restless | Mid-low energy, high onset density | | floating | Mid-low energy, bright | | driving | Mid energy, percussive | | soaring | Mid energy, harmonic...
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